- Master of Biotechnology Program, Department of Biology, University of Toronto Mississauga (UTM)
- Institute of Biomaterials & Biomedical Engineering
University of Toronto Mississauga (UTM)
3359 Mississauga Road
Mississauga, Ontario L5L 1C6
Dr. Jayson Parker teaches in the Master of Biotechnology Program at the University of Toronto Mississauga, the Institute of Biomaterials & Biomedical Engineering and the Faculty of Law.
His research interests involve a range of undergraduate and graduate students, as well as alumni across the University and include:
Clinical Trail Failure Rates
We have published a large body of research here that examines the risk of a drug failing during clinical trial testing and the factors that appear to impact the risk. We are constantly developing improved risk estimates for specific patient populations and believe this data will inform both public policy and research funding choices. Does personalized medicine reduce the risk of drug failure? What kind of data is the best indicator of the future success of a drug during testing?
Medical Device Ancestry
The regulation of the safety and performance of new medical device innovations is plagued with oversight problems that may threaten patient safety. This research traces specific medical devices back in time and reconstructs the history of their design evolution. Have small incremental changes over time to some medical devices lead to large design changes that have escaped proper safety oversight?
We are looking at a range of patent issues surrounding the use of new medical technologies. For example, patent strategies for new classes of drugs such as biosimilars. More recently, we are looking at the standards of evidence exercised by the patent office. Under what conditions are biosimilars patentable? How often are patent claims for a drug later to be found wrong? How often are erroneous patent claims actually corrected in the intellectual property landscape?
Dark Data & Wearable Health Technology
We are exploring several cutting edge issues with wearable technology in the digital health space that includes health (e.g. iWatchTM or HexoskinTM) and medical wearables (e.g. glucose pumps). Can we come up with a common data format for these devices that clinicians and manufacturers can agree upon as useful? Can we curate a database of user experience from these devices to explore new predictions of health and wellness?
“Dark data” refers to data with no useful purpose. Can such biometric data be predictive of health and wellness issues? How is this technology currently regulated and how does that impact product design choices?
Machine Learning & Neural Networks
This is a new area that overlaps with 2 research areas above. For both our clinical trial database and the impending biometric database we will build, we are starting to use machine learning to interrogate these data sets. Neural networks will be deployed against the biometric database.
Stratifying Hospital Technology by Memory Demands
We are stratifying hospital technologies based on their demands on temporary memory storage in humans. There are limits to how much temporary memory any person can handle at once. Have we created bottlenecks in hospital care where memory loads of health technologies, are too much for healthcare providers to realistically track to avoid catastrophic errors?